Rice (Oryza sativa) is a grain that comes in third place among all grains after corn and wheat. 80 percent of Indonesians eat rice as a staple diet, especially in Southeast Asian countries, but the International Rice Research Institute (IRRI) reports that farmers lose 37 percent of their rice crops each year owing to pests and illnesses. Based on this study, it is critical to investigate the detection of rice pests and illnesses. Using the Convolution Neural Network (CNN) technique, an automatic classification system to identify and predict plant illnesses has been developed. A study titled Classification of Rice Leaf Diseases was undertaken by the author. The CNN Algorithm is being used to help farmers learn how to combat rice leaf diseases. Bacterial leaf blight, Rice blast, and Rice tungro virus were among the rice leaf types classified in this study. There are 6000 datasets in all, with 80% of them being training data, 10% being validation data, and 10% being testing data. The accuracy of the results obtained for epochs 25, 50, 75, and 100 varies. The best training accuracy results come from epoch 100, which has a 98% accuracy rate, and testing using a confusion matrix has a 98% accuracy rate. In diagnosing rice leaf diseases, the Convolutional Neural Network (CNN) algorithm delivers great accuracy.
The delay in the absorption of village funds from the central government to the village government is due to the village government's difficulty preparing village development innovation programs. The innovation tradition will grow if the cycle of transformation of knowledge and acceptable practices from one village to another, especially villages with similar conditions and problems, can run smoothly. For the process of exchanging knowledge and experiences between villages to run smoothly, it is necessary to codify best practices in a structured, documented, and disseminated manner. This research aims to design an application that functions as a medium for sharing knowledge about the use of village funds through government innovation narratives. The application is expected to become a reference for villages to carry out innovative practices by conducting replication studies and replicating acceptable practices that other villages have done. Therefore, it is necessary to have a system requirements elicitation method that can explore the village's requirements in sharing knowledge so that the resulting system is of high quality and by the objectives of being developed. There are several Goal-Oriented Requirements Engineering (GORE) methods used, such as Knowledge Acquisition in Automated Specification (KAOS) and requirements engineering based on business processes. In this research, the KAOS method was demonstrated as the elicitation activity of a village innovation system. Then the results were stated in the Goal Tree Model (GTM). Model building begins with discussions with the manager of the village innovation program to produce goals. The goals are then broken down into several sub-goals using the KAOS method. The KAOS method is used for the requirements elicitation process resulting in functional and non-functional requirements. This research is the elicitation of the requirement for the village innovation system so that it can demonstrate the initial steps in determining the requirements of the village innovation system before carrying out the design process and the system creation process. The results of this requirement elicitation can be used further in the software engineering process to produce quality and appropriate village innovation applications.
The Elderly is someone who has reached the age of 60 years, the main health problem in the elderly is nutritional problems. Nutritional status is a measurement that can assess food intake and the use of nutrients in the body. One of the assessments of nutritional status in the elderly uses anthropometry with the type of measurement of Body Mass Index (BMI). Determination of nutrition is an effort to increase Life Expectancy (UHH). Therefore, a study will be conducted on the classification of nutritional status in the elderly using the Learning Vector Quantization 3 (LVQ 3) method with seven inputs used, namely: gender, age, Bb, Tb, BMI, social status and disease history, and five results of status classification nutritional status, namely inferior nutritional status, poor nutritional status, normal nutritional status, obese nutritional status, and very obese nutritional status. The best parameters used in this study are: learning rate (α) = 0.2, learning rate reduction = 0.4, window (ɛ) = 0.4 and minimum learning rate = 0.001, epoch = 1, 5, 10, 50, 100, 200, 500, 1000 with a comparison of the distribution of training and testing data of 80:20 on a total of 599 data. Based on the test results, the number of epoch values affects the accuracy results. The highest accuracy obtained is 86.67%. The calculations using the confusion matrix in this algorithm are 87% accuracy, 83% precision, and 81% recall. The Learning Vector Quantization 3 (LVQ 3) method can use to classify nutritional status in the elderly.
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